Chapter 1: Introduction

1.1 Background

Hospital admission and readmission are common phrases used in the healthcare settings. According to Western Australia [WA] Health Services (2012), an admitted patient to a hospital is a person who meets the admission criteria and is admitted through the hospital’s admission procedure to receive treatment within a given duration. A readmitted patient, on the other hand, refers to the patient who has been in inpatient and is then readmitted after being discharged within four weeks from the same establishment (WA Health Services, 2012). The older generation, starting from 65 years and above, has higher rates of hospital admission and readmission compared to the general population (Courtney et al. 2012). Researchers have noted that the continued increase in hospital admissions of the elderly is one of the crucial and multifaceted issues that are affecting health services such as the increasing healthcare costs and resources (Longman et al., 2012; Almagro et al., 2006). In this case, it is important to define and identify the key factors that constitute this increased health issue to facilitate effective intervention.

According to Courtney et al., (2012), patients who seek readmission experience a functional decline while at home, which has the capability to reduce the quality of life as well as their independence; thus increasing their readmission rate. The functional decline directly relates to the quality of life of an individual by causing adverse effects on social, physical, and emotional aspect of a person; thus, increasing the unplanned hospital readmission (Courtney et al., 2012; Bjorvatn, 2013). Functional decline is dependent on the way elderly patients perform their daily activities such as dressing and feeding as well as instrumental activities of daily living (Courtney et al., 2011; Cawthon et al., 2012). Courtney et al., (2011) and Beswick et al., (2008) argue that the core factor contributing to this problem is the reduced physical function that calls for continued hospitalisation, long-term nursing home care, as well as premature death.

Readmissions are costly for both the patient and the healthcare system (Chan et al., 2011; Halfon et al., 2006). According to Kassin et al., (2012), the increased hospital readmission of elderly patients within 30 days is a clear indication that there is a need for more improved health care research. The 30 days hospital readmission for the elderly patients (aged more than 65 years) relates to a number of risk factors that increase the readmission rate among the elderly patients (Park, Andrade, Mastey, Sun, & Hicks, 2014) which is the interest of this study to identify these risk factors. Various studies have suggested that demographic and clinical parameters tend to be risk factors for patient readmissions (Kassin et al., 2012; Tsai, Joynt, Orav, Gawande, & Jha, 2013; Fiscella, Burstin, & Nerenz, 2014). These clinical factors may include polypharmacy where one has more than five medications, using high-risk medications such as anticoagulants, having more than five chronic conditions, and possessing specific clinical conditions such as heart failure and depression (Aljishi & Parekh, 2014). Other risk factors for readmission may either be logistical and/or demographic factors. Such factors include race, reduced social network indicators (such as being with no family or friends) prior to hospitalisation, lower socioeconomic status, older age, and low health literacy (Aljishi & Parekh, 2014; Albrecht et al., 2014; Laurin, Moullec, Bacon, & Lavoie, 2012; Richardson et al., 2012; Shah et al., 2012). Therefore, it is also important to investigate possible psychological risk factors in relation to readmission such as depression as little has been done on these factors.

As unplanned hospital readmission increase, then health care costs will continue to increase as these patients continue using the services of the hospitals or healthcare facilities (Chan et al., 2011; Nelson & Pulley, 2015). As such, it is important to address this problem by attempting to reduce it. The most important thing to do in this case should be to introduce and enhance the discharge planning process in an attempt to reduce the risk factors associated with the transitioning from hospital to home (Sellers et al., 2013; Chan et al., 2011). The process should aim at improving the efficiency of health care services delivery to patients while reducing the high cost of readmission to hospital (Dorman et al., 2012; Ramsey & Hobbs, 2006; Volpato et al., 2011). Park et al., (2014) recommend that research should identify specific risk factors that can then be addressed. These factors can assist in identifying the possible solutions that can be implemented within the healthcare institution to reduce the unplanned hospital readmission (Kansagara et al., 2011).

1.2 Aim

The purpose of this study is to identify any risk factors for unplanned readmission to hospital in elderly patients over the age of 65 years.

1.3 Thesis Outline

Overview of the literature within the last eleven years in regards to risk factors for unplanned hospital readmission after discharge of patients from hospital is provided in chapter two. Chapter three introduces the research methods for a secondary analysis study of a current database to determine risk factors for unplanned readmission of people >65 years to hospital post discharge.

Chapter 2: Literature Review

2.1 Introduction

This literature review addresses the risk factors for unplanned hospital readmission associated with elderly patients (i.e., greater than 65 years of age). The literature review then provides a critical discussion of the literature within the last eleven years that relate to the risk factors for unplanned hospital readmission in elderly patients through a review of previous literature. Previous research has shown that the aging population is associated with an increase in hospital admissions (Garcia-Perez et al., 2011; Kristensen, Bech, & Quentin, 2015). Garcia-Perez et al., (2011) note that elderly patients increase both social and healthcare services. The authors in this study suggest that the increase in admission of this population increases the consumption of healthcare resources (Garcia-Perez et al., 2011). Factors that contribute to hospital readmissions relate to health-care factors as well as patient-related factors, and disease-related factors or a combination of these factors (Garcia-Perez et al., 2011; Bahadori & FitzGerald, 2007; Mehta et al., 2011). Some of these factors are avoidable only if the right interventions are taken while the disease-related factors are hard to control. Garcia-Perez et al., (2011) claim that defining and understanding these factors is an effective approach for identifying patients at risk of readmission to hospital. Besides, such an understanding is suitable for designing preventive interventions for improving the efficiency of the hospital to home transition (Garcia-Perez et al., 2011).

Robinson and Kerse (2012) found that interventions can assist in reducing the unplanned hospital readmission for the patients with more than 65 years. The results of the study revealed that out of the 95,318 patients admitted to the hospital under study, 66,983 are aged 65 years and older (Robinson & Kerse, 2012). This, therefore, calls for the need of taking into account the well-being of elderly people when planning the appropriate interventions to use in improving their health status. Unplanned hospital readmission for the elderly population has reached 10.8% in New Zealand (Robinson & Kerse, 2012). More important, the readmitted patients have severe issues and worse outcomes constituting to a higher morbidity and mortality rate (Robinson & Kerse, 2012).

2.2 Search Strategy

A search of the literature was performed from relevant resources for the risk factors of unplanned readmission of elderly patients to a hospital by an online search through the databases of EBSCO host including, Medline, Cinahl, Pubmed, PsycINFO, and the Cochrane Library. The search was conducted using the key words “risk factor*” AND “(elderly patients or older people) AND “hospital readmission”.

2.2.1 Inclusion

The study only included studies published within a period of 11 years (2005-2016), which were published worldwide and were accessible as full articles. Only peer-reviewed or published were included in this study. Further, the researcher included studies that had participants aged 65 years and above. The researcher included studies that had any of the following risk factors: psychosocial risk factors, demographic risk factors, and physical health risk factors, as they formed the major focus for this review.

2.2.2 Exclusion

Case studies and animals-based studies were excluded from this search. The researcher included full studies that were written in English only and excluded all studies written in different languages to avoid any difficulties in translations. Studies that had participants aged below 65 years were excluded. Studies that were not peer-reviewed were also excluded from this review. The researcher also excluded studies that did not major on readmissions, or those that focused on patient admissions. Community-based programs were also excluded from this study.

The search of the databases for risk factors for elderly patient hospital readmission, yielded a total of 493 articles. The review process, after exclusion of studies, concluded that 25 articles were suitable for inclusion based on the criteria in regards to health risk factors. The risk health factors studies were conducted in United Kingdom (UK) (n = 1), Spain (n = 1), the United States (US) (n = 16), Australia (n = 1), Switzerland (n= 1), China (n = 2), Italy (n = 2), and the Nordic countries (Iceland, Finland, Sweden, Norway, and Denmark) (n = 1).

For the articles addressing more than one risk factor, reviewing was independently done at each section of the study. Figure 1 shows the numbers of excluded articles and the reasons for the excluding for potential article addressing health risk factors.

Figure 1 Health Risk Factors Literature Review

2.3 Literature Review

2.3.1 Readmission among the Elderly

Unplanned hospital readmission is an emerging policy for the developing countries as a way of enhancing the safety and assessing the quality of health care (Kim et al., 2015; Steiner, 2015). Unplanned hospital readmission can occur in the same hospital where the patient was first admitted to or to a different hospital. Kim et al., (2015) claim that one-fifth of the U.S. Medicare beneficiaries are those patients hospitalised after 30-day of discharge; in 2003 and 2004, 35% patients were readmitted in a duration of 90 days. Kim et al., (2015) conducted a study involving a sample of 509775 patients aged 50 years or more. Out of these patients, 59556 (11.7%) were readmitted within 30 days of discharge from the hospital where 79% were readmitted to the same hospital and 21% to a different hospital (Kim et al., 2015). The cause of this trend was due to clinical factors such as the quality of healthcare services and non-clinical factors such as adherence to medication after discharge (Kim et al., 2015). Therefore, these are the possible causes of unplanned patient readmission to different hospitals as these patients claim that they experienced more fragmented care in these hospitals (Kim et al., 2015).

According to the World Health Organisation [WHO] (2005), elderly people comprise the largest percentage of the patients admitted to acute hospitals. For instance, patients aged 65+ years constitute 36% of hospital admissions (WHO, 2005). The key reason for such admissions is the reduced physical functioning, which cannot be improved during the discharge time (WHO, 2005). The article further states that hospital readmission and multiple admissions contribute to the increased general use of the hospital beds (WHO, 2005). For instance, in the UK, patients with a history of more than two history of hospital admissions encounter at least 20 fold increases with the risk of unintended hospital admission constituting 38% of all readmissions (WHO, 2005). The WHO (2005) recommends that proper hospital discharge management is an effective measure for curbing this trend as this helps in facilitating an effective and safe transference of the elderly people from in-patient hospital care to home-based care. Such measures according to this study are important as they play a substantial role in preventing subsequent readmission (Berenson, Paulus, & Kalman, 2012; WHO, 2005).

Ziebarth (2015) carried out a study of the factors affecting hospital readmissions and possible measures that can be used to reduce them.  In this journal, Ziebarth (2015) claims that the implementation of Patient Protection and Affordable Care Act (PPACA) has facilitated a variety of changes in the healthcare institutions that in turn have affected the beneficiaries of the Medicare program, which attempt to control the risk factors for hospital readmission. “Hospital readmissions affect over 80 percent of all Medicare enrolees (Ziebarth, 2015, p. 1).” People who are readmitted mostly are elderly patients and having multiple chronic diseases (Ziebarth, 2015; Kociol et al., 2012; Locker, Baston, Mason, & Nicholl, 2007; Kapella, Larson, Patel, Covey, & Berry, 2006). Leppin et al., (2014) affirm these claims by asserting that early hospital readmission is common among the high-risk and elderly patients. Howell, Coory, Martin, & Duckett, (2009) assert that the prevalence of the readmission of the patients with chronic diseases can be contributed to by a variety of factors such as inadequate follow-up care, deficient dependence on family caregivers, and poor patient agreement with care instructions among other reasons.

2.3.2 Costs of Patient Readmission

Davies, Saynina, McDonald, & Baker (2013) carried out a study on the challenges of readmitting a patient to the same hospital. According to these authors, 20% of the hospital Medicare patients fall into unplanned readmission to an acute care facility within a duration of 30 days after discharge from these hospitals. This leads to spending of additional $15 billion by CMS (Centres for Medicare and Medicaid) since readmission is a prominence quality metric of these hospitals, as it is used to assess the performance of these hospitals (Davies et al., 2013). Besides, unplanned readmission to a different hospital takes place for 30% of all the unplanned readmissions in U.S. (Davies et al., 2013). Due to the increasing clinical factors such as the quality of healthcare services and non-clinical factors such as adherence to medication after discharge (Davies et al., 2013; Kwaan et al., 2013). A different study by Leppin et al., (2014) disclosedhat reducing the unplanned readmission is a key priority aimed at improving the quality of healthcare services in the U.S. One out of five Medicare beneficiaries, or people benefiting from the Medicare insurance program in the U.S., according to this study are people subjected to unplanned readmission to hospital within 30 days, which increases the overall cost of the program by $26 billion per annum (Davies et al., 2013). As a way of curbing this risk and increasing the quality of healthcare services, the U.S. government has made the 30 days readmission to hospital a national priority, by adopting policies and programs such as imposing penalties to the hospitals with higher readmission rates (Davies et al., 2013; Zuckerman, Sheingold, Orav, Ruhter, & Epstein, 2016). In addition, imposing penalties to the hospitals with higher 30 days readmission rates have been found as the most effective way of reducing readmission of patients within 30 days (Leppin et al., 2014; Jencks, Williams, & Coleman, 2009; Vilaro et al., 2010), according to these authors, such interventions facilitate self-care for these patients when transitioning from hospital to home.

Along the same argument, Zuckerman (2016) addshat hospital readmission within 30 days in America constitutes to increased Medicare expenditures to approximately $17 billion per year. To avoid this, Zuckerman (2016) states that most of these readmissions can be avoided through effective changes in hospital care such as improving the discharge planning and facilitating the follow-up process of discharged patients (Zuckerman, 2016; Jha, Orav, & Epstein, 2009; Tuso & Beattie, 2015). Moreover, policies such as the Affordable Care Act (ACA) have constituted to a massive reduction of the readmitted patients per year (Zuckerman, 2016). According to this study, the number of patients readmitted in 2010 to 2015 reduced by 565,000 (Zuckerman, 2016) The ACA policy introduced the Hospital Readmissions Reduction Program that targeted the reduction of readmission for patients with pneumonia, heart failure, and heart attack (Zuckerman, 2016). These patients according to the author of the article experienced numerous benefits as hospitals responded to this new incentive (Zuckerman, 2016). The policy aimed at countering both clinical factors such as quality of the healthcare services and non-clinical factors such as adhering to the medical instructions after discharge (Zuckerman, 2016; Herrin et al., 2015).

2.3.3 Measurement of Readmission among the Elderly across Studies

Fauci et al., (2011) evaluated factors causing the increased unplanned readmission for the Epithelial Ovarian Cancer (EOC) among the elderly patients arguing that complications incurred after the operation increasing the probability of readmitting these patients again. Thromboembolic events and wound complications are the key causes of readmission of the EOC patients (Fauci et al., 2011).

The unplanned hospital readmission is an effective approach to measuring the quality of healthcare services (Robinson & Kerse, 2012; Hasan et al., 2010). As a result, increased unplanned readmissions after discharge is a clear show of care deficiencies. Most of the causes of the readmission can be controlled and prevented especially those ones caused by the lower quality of healthcare services in hospitals (Robinson & Kerse, 2012; Coleman, Parry, Chalmers, & Min, 2006: Zuckerman et al., 2016). Nevertheless, the study claims that the proportion of the readmitted patients capable of being prevented range from 5-71% (Robinson & Kerse, 2012).

2.4 Risk Factors for Hospital Readmission of Elderly Patients

An ageing population increase the rate of unplanned hospital readmission (Campione, Smith, & Mardon, 2015). This necessitates the need for identifying the factors affecting the risk of rehospitalisation; thus, simplifying the identification of the individuals at risk and appropriate course of actions in the intervention of this challenge. Campione et al., (2015) disclose that there is a need for identifying the risk factors that exacerbate the unplanned hospital readmission as it increases unnecessary health costs. As such, this section of the study will assess the risk factors causing and increasing the readmission of elderly patients. To facilitate this process, risk factors will be categorised under three groups: psychosocial risk factors, demographic risk factors, and physical health risk factors.

2.4.1 Psychosocial Health

Psychosocial health refers to the mental, social, and spiritual welfare of a patient (Gudmundsson et al., 2006). The literature reviewed in this section relates to the risk factors affecting these aspects of the psychosocial health of elderly patients. Specifically, the studies have assessed the articles which have discussed both anxiety and depression of patients, stress levels, health literacy, and social support and how they contribute to the increased unplanned hospital readmission.

2.4.1.1 Anxiety

Anxiety refers to the feelings of unease such as worry that can turn out to be severe. This is a prevalent feeling among the sick people especially the elderly patients (Balcells et al., 2010). These people get worried of their deteriorating health, which can be constant resulting to the adverse effects. This section of the study evaluates whether there is a connection between anxiety and unplanned hospital readmission of the elderly people.

Four studies (Balcells et al. 2010; Coventry, Gemmell, & Todd, 2011; Sharif, Parekh, Pierson, Kuo, & Sharma, 2014; Gudmundsson et al., 2006) examined anxiety in relation to the unplanned readmission to hospital of elderly patients. Three of the studies found anxiety as a significant factor for the increased unplanned readmission for the elderly patients (Balcells et al. 2010; Sharif et al., 2014; Gudmundsson et al., 2006). Measuring anxiety for this study was facilitated using a Spanish version of the Hospital Anxiety and Depression Scale (HADS) (Balcells, et al., 2010). HADS is an authenticated screening questionnaire used for psychiatric morbidity. The questionnaire contained a subscale for the anxiety with the score ranging from 0-21. The participants were assessed using St. George’s Respiratory Questionnaire (SGRQ) and grouped into three groups: activity, symptoms, and impact (Gudmundsson et al., 2006). The symptoms group was the one used to measure the anxiety of the patients as its scores were used to measure the suffering caused by respiratory symptoms. If the score was higher than or equal to 8, then the anxiety symptoms of the patient was said to be mild; thus the patient was suffering from anxiety (Balcells, et al., 2010).

Balcells et al., (2010) used a sample size of 337 COPD patients (Chronic Obstructive Pulmonary Disease) and measured for the unplanned readmission rate over 2 years. The study found that symptoms of anxiety among these patients was higher compared to the depressive symptoms, which were 27% and 14% respectively. Balcells et al., (2010) demonstrated that anxiety is a key cause of treatment failure in the first exacerbation. Balcells, et al., (2010) add that patients with anxiety report worse results at any stage of sickness. Another study was prospective study by Gudmundsson et al., (2006) involved 416 patients followed for one year. The article found anxiety, as s significant factor, which was higher in women compared to men (47% and 34% respectively). According to this study, patients with higher levels of anxiety were found to have poor health status. More than half of the study population were suffering from anxiety where one sample containing 202 patients found that 81 were suffering from anxiety (Gudmundsson et al., 2006). Sharif et al., (2014) conducted a retrospective cohort study on 8263 elderly patients to assess their anxiety. The study by Sharif et al., (2014) concluded that anxiety is a significant factor for unplanned hospital readmission of elderly people. The study found that the risk of unplanned hospital readmission for the patients suffering from anxiety was 2.5 times higher than that of normal patients (Sharif et al., 2014). In addition, patients suffering from anxiety have poor compliance with the medications; thus increasing the 30 days readmission rate.

On the other hand, Coventry et al., (2011) argue that anxiety is more prevalent for COPD patients as well the discharged AECOPD patients (An acute Exacerbation of Chronic Obstructive Pulmonary Disease). The article is a cohort study and used 79 participants in this study for a year in the U.K. However, this study found that anxiety is not a significant factor for the increased rate of unplanned readmission.

Four studies examined anxiety and three found a significant relationship with the increased unplanned readmission of elderly patients while one study did not show that anxiety does not lead to the increased readmission of the elderly patient that could be a result of small sample size in the study.

2.4.1.2 Depression

Depression is a prevalent health condition that is associated with mood disorder. Depression affects how a patient think, feel, and handle everyday activities (Balcells et al., 2010). With these side effects, it is evident that depression has adverse effects on the patients, phenomena that determines how they perceive their care. As such, this sub-section of the study evaluates the impact that depression has on the unplanned hospital readmission of the elderly patients.

Four studies (Balcells et al., 2010; Coventry et al., 2011; Sharif et al., 2014; Gudmundsson et al., 2006) examined depression in relation to the unplanned readmission to hospital of elderly patients. All of these studies reveal that depression was a significant factor for the increased unplanned hospital readmission. Measuring depression for these studies was facilitated using a Spanish version of the Hospital Anxiety and Depression Scale (HADS) (Balcells, et al., 2010). HADS is an authenticated screening questionnaire used for psychiatric morbidity. The questionnaire contained a subscale for the depression with the score ranging from 0-21. The participants were assessed using St. George’s Respiratory Questionnaire (SGRQ) and grouped into three groups: activity, symptoms, and impact (Gudmundsson et al., 2006). The symptoms group was the one used to measure the depression of the patients as its scores were used to measure the suffering caused by respiratory symptoms. If the score was higher than or equal to 8, then the depression symptoms of the patient was said to be mild; thus the patient was suffering from anxiety (Balcells, et al., 2010).

One retrospective cohort study by Sherif et al., (2014) used a sample size of 8,263 patients followed over 12 months and found that 741 patients (8.9%) were readmitted due to depression. The study adds that depressed patients are poor in adhering to the prescribed medications after the discharge. Balcells et al., (2010) added that patients with high rates of depression are at a higher risk of being readmitted as they have poor Health-Related Quality of Life (HRQoL). This study found that 14% of the study participant were suffering from depression, where they linked this to the increased unplanned hospital readmission (Balcells et al., 2010). Gudmundsson et al., (2006) also found COPD patients often report cases of poor health status and depression to be the cause of unplanned hospital readmission. Adding on the same, Gudmundsson et al., (2006) claim that the readmission rate of the depressed patient is 21.6% compared to the normal patients whose readmission rate is 17.5%. Coventry et al., (2011) demonstrate that depression is also a major factor that causes high readmission rates among the COPD patients.

Four studies examined depression and all found a significant relationship with the unplanned hospital readmission of elderly patients. All of these studies (Balcells et al., 2010; Coventry et al., 2011; Sharif et al., 2014; Gudmundsson et al., 2006) revealed a close association between unplanned hospital readmission and depression.

2.4.1.3 Stress Levels

Stress refers to the feeling of being under excessive emotional or mental pressure (Edmondson, Green, Ye, Halazun, & Davidson, 2014). Stress levels affects the way an individual think, behave, feel, and even the way their body works (Edmondson et al., 2014). The study evaluates whether there is a relationship between the stress levels and unplanned hospital readmission of the elderly patients.

Two studies (Sharif et al., 2014; Edmondson et al., 2014) examined stress levels in relation to the unplanned readmission to hospital of elderly patients. Only one of these studies revealed that stress was a significant factor for the unplanned hospital readmission. An observational cohort study by Edmondson et al., (2014) involved 342 ACS patients (Acute Coronary Syndrome) in the U.S, where 40 patients (11.7%) were readmitted due to high stress levels. The measurement of stress in this study required the ACS patients to report on their stress levels during and after hospitalization. To facilitate the reporting process, the patients were to provide dichotomized responses (rarely, often, or never) on whether they felt tensed during or after discharge.

Edmondson et al., (2014) state that the key risk factor for elderly patients is due to the increased stress rates after one is diagnosed with ACS. According to Edmondson et al., (2014), stress levels during hospitalisation increase the risk of readmission for these patients within 30-days after they are discharged; thus showing that stress is a significant factor. Ideally, admission of the ACS is a stressful occurrence following the strange surroundings, fear, and loss of control (Edmondson et al., 2014). Stress levels increase not only due to the illness but also due to being admitted to hospital, as this is associated with more physiological and psychological stresses. As a result, the psychological system of the patient after discharge is impaired while their reserves are exhausted; thus, they cannot protect them from health threat, hence increasing the readmission rate (Edmondson et al., 2014). Ascertaining these claims, the study found that out of 342 patients, 22 patients (6.4%) reported an instance of high stress. In addition, patients with high stress levels (5 events, 23%) were readmitted within 30 days of discharge while those with low stress levels (35 events, 11%) were also readmitted. This clear shows that stress levels increase the readmission rate. However, Sharif et al., (2014) argue that the key causes of readmission of patients are the provider, patient, and system factors of which they claim that high stress levels is a major aspect of the readmitted patients.

Two studies examined stress levels and one found that stress levels is a significant factor for unplanned hospital readmission.  The study by Edmondson et al., (2014) revealed that stress levels was a significant factor for the unplanned readmission while the other study did not show any relationship between unplanned readmission with stress levels (Sharif et al., 2014).

2.4.1.4 Health Literacy

Health literacy refers to the degree to which one can acquire, process, and comprehend the basic health information for one to make sensible health decisions (Mitchell, Sadikova, Jack, & Paasche-Orlow, 2012). This significant factor determines the way health is perceived in society. The study at this section assesses whether health literacy is related to the unplanned hospital readmission of the elderly patient.

One study (Mitchell et al., 2012) examined health literacy in relation to the unplanned readmission to hospital of elderly patients. The study is a secondary data from Project RED trials study (Re-Engineered Discharge) and it consisted of 703 participants where 138 of the participants (20%) had low health literacy while 207 participants (29%) had best health literacy, and 358 of the participants (51%) had reasonable health literacy (Mitchell et al., 2012). The authors in the study claim that low health literacy, which is the way a patient obtains and understands the fundamental health materials and information, contributes to the increased mortality rate, poor self-management, and higher rates of hospitalisation for chronic diseases (Mitchell et al., 2012). In US hospitals, unplanned hospital readmission is a common incidence according to the study (Mitchell et al., 2012). The study notes that approximately 20% of the Medicare patients are readmitted within a duration of 30 days after the discharge (Mitchell et al., 2012). The study emphasises that patients with low health literacy have a higher probability of returning to the hospital after discharge. (Mitchell et al., 2012). Justifying these claims, Mitchell et al., (2012) argue that this is as a result of failing to understand the discharge instructions once discharged; thus, failing to follow the given prescriptions, which has a direct impact on the increased rate of 30-day hospital readmission.

The reviewed study (Mitchell et al., 2012) has shown substantial connection between health literacy and unplanned hospital readmission. This, therefore, means that health literacy is a significant factor for the unplanned hospital readmission.

2.4.1.5 Marital status

Marital status is another important factor that the study aims at assessing. Marital status relates to a condition of being married, divorced, or unmarried. The status that one is determines the rate of unplanned hospital readmission (Garrison, Mansukhani, & Bohn, 2013).

One study (Garrison et al., 2013) examined marital status in relation to the unplanned readmission to hospital of elderly patients. The retrospective case-control study found marital status as a significant factor for the unplanned hospital readmission. The study used a sample size of 276 family medicine inpatients followed for 12 months in the U.S where the participant’s ratio of 0.54 revealed that they feel a protective effect from their family members and relatives.

Garrison et al., (2013) indicate that the marriage status is important in complementing the hospital services for the elderly patients; thus determining the readmission rates among elderly patients. Hospital to home transition is a core factor determining the unplanned readmission rate for patients with over 65 years (Garrison et al., 2013). This process requires someone who is in a position of interpreting the discharge medications in a proper way for the elderly patients to understand and adhere to for more positive results, which will reduce the chances of being readmitted. Measurement of the impact of marital status on the risk of readmission where patients were admitted to a family medicine for the inpatient services. The role of inpatient registry was to identify the potential controls and cases for the unplanned hospital readmission. The family medicine was to evaluate the impact that protective care has over an elderly patient and the capability that it has on reducing readmission rate. Upon the completion of the study, most of the readmitted patients were not married compared to the controls with the results being 44% vs. 57.8%. Justifying these results, Garrison et al., (2013) explained that being married has a protective effect on the odds of readmission according to the 30 days readmission. This is important if a discharged patient has a person to facilitate the transitioning stage, hence reducing the chances of unplanned hospital readmission. (Garrison et al., 2013). The protective effect of being married according to the study is significant in reducing the odds of living alone that increase the risk of being readmitted. The study found that out of 192 patients, 47 who are either single, divorced, or widowed are readmitted to the hospital within 30 days while the married patients readmitted were 37. These results show a significant effect that the marital status has on influencing the 30 days readmission rate.

One study examined social support and found a significant relationship with the unplanned hospital readmission of elderly patients.

2.4.2 Demographic Factors

Demographic factors comprises of age, race, gender, language, and ethnicity as well as socioeconomic status such as education and income level (Cornette et al., 2005). The studies in this section focus on the demographic factors that increase the risk of unplanned hospital readmission of the elderly patients. The factors of interest for this study are age, sex, income and living arrangement as they directly influence the readmission rate.

2.4.2.1 Age

Ten studies (Kirby, Dennis, Jayasinghe, & Harris, 2010; Silverstein, Qin, Mercer, Fong, & Haydar, 2008; DePalma et al., 2013; Fisher et al., 2013; Emons et al., 2016; Clendennen, Bowden, Griggs, Morgan, & Umstattd Meyer, 2015; Van Gestel et al., 2012; Cornette et al., 2005; Chawla, Bulathsinghala, Tejada, Wakefield, & ZuWallack, 2014; Mitchell et al., 2012) examined age in relation to the unplanned readmission to hospital of elderly patients. Out of the ten studies, six studies found age as a significant risk factor for the unplanned hospital readmission (Kirby et al., 2010; Silverstein et al., 2008; Fisher et al., 2013; Emons et al., 2016; Clendennen et al., 2015; Cornette et al., 2005). Four of the reviewed studies did not show any significant relationship between age and unplanned readmission. All of these studies assesses the relationship between age and readmission rate by evaluating the readmission rate of the elderly patients who are over 65 years of age.

A study by Cornette et al., (2005) used 625 patients followed over 3 months in the U.S. According to the prospective observational study, 24% of the patients elderly than 65 years were readmitted to hospitals within six months while 27% of the patients over 75 years are readmitted to surgical settings (Cornette et al., 2005). In addition, 6% of the patients over 65 years are readmitted within 30 days of discharge (Cornette et al., 2005). Silverstein et al., (2008) state that patients with over 75 years have a higher risk of being readmitted to hospital. Moreover, patients advanced age (<65 years) have are prone to higher readmission rates (Cornette et al., 2005).

However, studies in the U.S by Chawla et al., (2014), Van Gestel et al., (2012), and Mitchell et al., (2012) concluded that age is not a significant factor to unplanned hospital readmission it might because they used a small sample size in comparison with the six studies that found age is a significant risk factor.

Ten studies examined age and six found a significant relationship with the unplanned hospital readmission of elderly patients. These studies claimed that as the age increases, the risk for the unplanned readmission increases. However, three studies failed to establish a connection between age and unplanned readmission as they evaluated age as a significant factor and found other factors as significant.

2.4.2.2 Income Levels

Four studies (McFarland, Ornstein & Holcombe, 2015; Campione et al., 2015; Silverstein et al., 2008; Kirby et al., 2010) examined income level in relation to the unplanned hospital readmission of the elderly patients. Income levels determine the capability of an individual to access healthcare (McFarland et al., 2015). The ability of an individual to receive quality health depends on the social class that one belongs, which is determined by their income levels (McFarland et al., 2015).

According to McFarland et al., (2015), income level is a key determinant of the readmission rates, as it determines the quality of care that a patient receives. Through the Affordable Care Act policy in 2010 in America, government payments to physicians depend on metrics of assessing the efficiency and quality of healthcare services provided as a way of motivating value-based healthcare services (McFarland et al., 2015). Their research notes that the quality of services that a patient receives depends on the hospital that one attends, depending on the cost of provided services (McFarland et al., 2015). Campione et al., (2015) add that hospitals serving vulnerable populations in a society with a low socioeconomic status have higher readmission rates. This study used sample size contained 2592 participants in U.S. (Campione et al., 2015).

McFarland et al., (2015) add that low income earners seek poor healthcare services depending on their affordability and that elderly patients are prone to this trend following the fact that most of them do not work or taken care of by their relatives. In turn, this situation increases their unplanned hospital readmission since the quality of services they receive is not sufficient to their healthcare needs. A retrospective cohort study in the U.S carried out 29292 elderly patients for two years by Silverstein et al., (2008) also affirm these claims by eluding that most of the elderly patients with a higher readmission rate are low-income earners. The economically disadvantaged patients are readmitted back to hospitals following the quality of services provided to them at the initial admission period as well as the ability to afford prescribed medications after hospital discharge (Kirby et al., 2010). The retrospective study in Australia, used 16000 patients for a year emphasises the need for quality health services during the primary care as it determines whether the individual will be readmitted patients to hospital or not (Kirby et al., 2010). Despite these claims, Kirby et al., (2010) does not establish a relationship between income level and the unplanned hospital readmission.

Four studies examined the income levels and three studies found a significant relationship with unplanned hospital readmission. The three studies income level as a significant factor by claiming that income level determines the services that one get, where most of the elderly patients are low-income earners or from low socioeconomic status. However, one of the study failed to establish a connection between hospital readmission and income level.

2.4.2.3 Gender

Gender variation is being either a male or a female, which is a major concern of the study. At this section, the study aims at evaluating whether the unplanned hospital readmission of the elderly patients is equal across all the gender.

Eight studies (Fisher et al., 2013; DePalma et al., 2013; Wong, 2015; Cornette et al., 2005; Chawla et al., 2014; Clendennen et al., 2015; Kirby et al., 2010; Silverstein et al., 2008) examined gender in relation to the unplanned hospital readmission of the elderly patients. Five studies found gender as a significant factor for the unplanned hospital readmission (Wong, 2015; Cornette et al., 2005; Clendennen et al., 2015; Kirby et al., 2010; Silverstein et al., 2008) while the other three did not. A study by Silverstein et al., (2008) contained 29,292 patients followed over 2 years, where gender was found as a significant factor. Silverstein et al., (2008) suggest that gender is possibly related to the unplanned readmission of patients (Silverstein et al., 2008). The study stress on gender as a risk factor by claiming that male patients stand a higher chance of being readmitted (Silverstein et al., 2008). A prospective observational study in U.S within three months for 625 patients by Cornette et al., (2005) found that male sex is a core factor contributing to the early unplanned hospital readmission. Kirby et al., (2010) introduces new aspect of unplanned readmission pertaining the gender. In this retrospective study in Australia within a year for 16000 participants, Kirby et al., (2010) claim male gender was common in the emergency department for the readmission purposes.

On the other hand, the three studies that disagreed that gender is a significant factor. Two of them were in U. S and they had small sample size (111 and 54 participants) within a short period (a week and 30 days) (Fisher et al., 2013; Chawla et al., 2014). A study in Italy used 522 patients found that gender is not a significant factor for unplanned readmission to hospital (DePalma et al., 2013).

Eight studies evaluated gender and four found it as a significant factor for the unplanned hospital readmission. The other three studies did not establish a relationship between gender and unplanned hospital readmission.

2.4.2.3 Living Arrangement

Three studies (Franchi et al., 2013; Wong, 2015; DePalma et al., 2013) examined living arrangement in relation to the unplanned hospital readmission of the elderly patients. Two of these studies were significant and one of the study did not show relationship between living arrangement and unplanned hospital readmission. To evaluate this relationship, the studies evaluated the readmission rate for the patients who were living alone and those who were not living alone. Under this measurement, the studies evaluated the impact that living with others had on the readmission rate as well as the readmission rate for the individuals who were living alone (DePalma et al., 2013). This was facilitated by the supervision of the living arrangements after the patient was discharged and their readmission rate (Wong, 2015).

The retrospective cohort study in China by Wong (2015) used a sample of 368 individuals who were followed for 2 year found that 37% of the displaced patients were readmitted to the hospital. Moreover, the study claims that unplanned hospital readmission rate was higher for the patients living alone as well as for those without supervision of their living arrangement after discharge. Besides, a study in Italy by DePalma et al., (2013) noted that lack of regular maternal or emotional support as well as living alone lead to higher rates of unplanned hospital readmission. DePalma et al., (2013) add that living alone increases the odds of hospital readmission for the elderly patients, as they do not get protection from anyone by living by themselves. The living arrangement determines the way one responds to the medication; thus, the elderly patients who were living alone had minimum supervision or guidance. This resulted to poor response to the medications. However, Franchi et al., (2013) conducted a study in Italy for 1178 patients followed for three months concluded that place of residence is not a significant factor for elderly people to be readmitted to hospital.

Three studies examined living arrangement and two found it significant to unplanned hospital readmission. The two studies linked living arrangement with the support that the patient received from society or family members (DePalma et al., 2013).

2.4.3 Physical Health

Physical health entails the health state of an individual by considering whether an individual is physically healthy or not (Allaudeen, Vidyarthi, Maselli, & Auerbach, 2011). Physical health is important in evaluating the health status of a patient by assessing factors such as physical activity, alcohol and drug use, dieting and nutrition, and medical self-care (Van Gestel et al., 2012). To gain a deeper insight of how these factors increase the readmission risk, this section evaluates the following risk factors: alcohol consumption, daily activity, diet, and comorbidities.

2.4.3.1 Comorbidity Diseases

Comorbidity condition refers to an existing medical condition, either independently or simultaneously (Franchi et al., 2013). This is a key factor that the study aims at evaluating and determine whether it has an effect on the unplanned hospital readmission of the elderly patients.

Four studies (Franchi et al., 2013; Allaudeen et al., 2011; Zai et al., 2013; Emons et al., 2016) examined comorbidity diseases in relation to the unplanned hospital readmission of the elderly patients. All of these studies found that comorbidity diseasesheart failure, stages of cancer, and hypoglycaemia as a significant factor for the unplanned hospital readmission. A cohort study in U. S by Zai et al., (2013) used hundred patients with heart failure as the sample size and followed them for three years where 38% of the study participants were readmitted to the hospital in a duration of 30 days; thus making comorbidities to be a significant factor.

A retrospective observational study in U.S followed for two years for 10359 patients by Allaudeen et al., (2011) found that patients with disease (stages of cancer) for example, are at a higher risk of being readmitted in conjunction with those suffering from cognitive heart failure and weight loss. Moreover, Emons et al., (2016) also carried out research aiming at identifying the risk factors associated with hypoglycaemia readmissions. The retrospective observational study in U.S used sample of 4476 participants for five years found that 24.5% of Emergency Room (ER) patients were readmitted in 30 days where 14.4% of this number were suffering from hypoglycaemia. Concluding the study, the authors claim that factors leading to the hypoglycaemia readmissions for the patients suffering from diabetes include: adults older than 45 years, exposure to NH/SNF/hospital, and respiratory and cardiovascular-related comorbid conditions (Emons et al., 2016). The last study used 1178 patients and followed for three months by Franchi et al., (2013) evaluated the risk factors associated with readmission for elderly adults in geriatric wards and internal medicine. According in this study, 19% of the patients are re-hospitalised within 90 days after release for the diabetic patients. Through a multivariate analysis in the study, comorbidity and severity index as well as Cumulative illness, as well as drug use, liver and vascular diseases were the key risk factors for the increased readmission for the elderly patients were revealed as significant factors (Franchi et al., 2013). Besides, Franchi et al., (2013) claim that liver and vascular are major comorbidities causing increased hospital readmission among the elderly people.

Four studies examined comorbidity diseases such as heart failure, stages of cancer, and hypoglycaemia and all found that it is a significant factor for the unplanned hospital readmission.

2.4.3.2 Exercise

Exercises refers to the physical activities that are important to maintain a healthy immune system. Physical activity is prerequisite for reducing risk levels of chronic diseases such as diabetes; assist in controlling weight, and boost the ability to attend to daily activities especially for the elderly adults (Van Gestel et al., 2012).

Three studies (Van Gestel et al., 2012; Chawla et al., 2014; Zai et al., 2013) examined exercise in relation to the unplanned hospital readmission of the elderly patients. Two studies (Van Gestel et al., 2012; Chawla et al., 2014) proved exercise as a significant factor for the unplanned hospital readmission. Chawla et al., (2014) measured the physical activity of the study patients by giving them an ActiGraph GT3X+ accelerometer for them to wear on the wrist just like a wristwatch for 30 successive days. The device was to measure the patient’s movements in three planes; thus facilitating the detection and measurement of the patient’s physical activity. The vector magnitude units (VMU) summed the movements of the three planes every minute where the recordings were downloaded from the accelerometer using Actilife software for further analysis.

Lack of physical activity is a noticeable aspect for elderly patients (Van Gestel et al., 2012). A study in Switzerland for 70 patients followed for seven days argued that a reduction in the physical fitness according to Van Gestel et al., (2012) leads to a shift in lifestyle due to low daily physical activities (PA); thus inducing a circle of reduced exercise constituting to the reduced activity levels. Besides, most of the readmitted patients revealed that different heart failure symptoms such as fatigue, lack of exercise, irregular heartbeat, and shortness of breath, which are all linked to reduced exercise, were the key cause of hospital readmission according to a cohort study in U.S. for 100 participants surveyed for three years by Zai et al., (2013). A study conducted in U.S within 30 days by Chawla et al., (2014) revealed that patients with lower physical activity levels a week after discharge stood a chance of 6.7 times of being readmitted within 30 days. Physical inactivity limits the functionality of an individual. The study assessed the impact of physical activity over the unplanned hospital readmission rate of patients. Those patients who were physically active on a weekly basis reduced the readmission rate (p<0.01) compared to those patients who were not active with their readmission rate being higher (p=0.06) (Chawla et al., 2014). This is a similar pattern revealed by the patients who increased their daily physical activities by 13 minutes reducing the likelihood of being readmitted again (Chawla et al., 2014). Patients who do not leave the house, by staying indoors without doing anything as they get served by home carers or their relatives, after being discharged are at risk of being readmitted as this limits their physical activity (Chawla et al., 2014; Van Gestel et al., 2012).

Three studies examined exercise and all found that it is a significant factor for the unplanned hospital readmission. These studies have affirmed that lack of exercise increases the unplanned hospital readmission (Van Gestel et al., 2012).

2.4.3.3 Dietary

Diet is an important factor to examine and determine whether it has an effect on the unplanned hospital readmission of the elderly patients. It is important for the elderly patients to take the right meal after discharge to facilitate the recovery process. In this section, the study reviews articles that discuss the relationship between this variable and the readmission rate.

Two studies (Kollipara, 2008; Zhuang et al., 2015) examined dietary in relation to the unplanned hospital readmission of the elderly patients. The two study found dietary as a significant factor for the unplanned hospital readmission. The measurement of the impact of dietary in the readmission rate was facilitated using a dietary test for the study participants to answer. The scores ranged from 0 to 10 that was used to reveal the dietary knowledge of the patients. The retrospective cohort study by Kollipara (2008) used 97 patients and followed them in one year in U. S. The study found that the unplanned hospital readmission was three times higher for the patients with insufficient dietary information. Moreover, a prospective study in China for 376 patients followed by a year by Zhuang et al., (2015) show the significance of dietary by disclosing that patients with nutritional risk preoperatively stand a higher chance of being readmitted within 30 days of discharge.

Malnutrition is a common phenomenon in acute hospitalisation constituting to the increased readmission rates and prolonged hospital stay (Kollipara, 2008). With the increasing risks of obesity, most of the admitted patients overfeed and have unhealthy eating practices; thus increasing obesity (Kollipara, 2008). As a way of improving the quality of service delivered to the patients with higher risk of readmission, diet should be assessed (Kollipara, 2008). Poor dietary is a major factor that slows the healing of a patient while at the same time increasing the risks of complications. As a result, patients who receive poor dietary stand a position for prolonged hospital stay as well as being readmitted after discharge (Zhuang et al., 2015). Failure to meet proper dietary has contributed to some of the patients failing to feed themselves, which has increased the readmission rate among the elderly patients. Kollipara (2008) argues that low health literacy is a factor affecting the patient’s diet in urban public hospitals. For instance, the study suggest that inadequacy of sodium knowledge, which is under dietary, increases the risk of readmission for the discharged patients with heart failure. However, these studies has linked dietary with health literacy; thus the need for more to determine whether dietary on itself influences the unplanned readmission.

Two studies examined dietary and found it is significant factor for the unplanned hospital readmission. Poor dietary increases the likelihood of the unplanned hospital readmission for the elderly patients who were either malnutrition or obese due to poor dietary; thus increasing the risk of being readmitted (Kollipara, 2008).

2.4.3.4 Alcohol Consumption and Smoking

Smoking and alcohol consumption are two other factors that the study aims at assessing whether they contribute to the increased unplanned hospital readmission among the elderly patients. In this case, the study examines articles that address their effect on the readmission rates.

Three studies (Franchi et al., 2013; Whitlock, 2010; Sharif et al., 2014) examined alcohol consumption and smoking in relation to the unplanned hospital readmission of the elderly patients. Only one of the study by Whitlock (2010) revealed that both alcohol consumption and smoking as the significant factors of the unplanned hospital readmission. The retrospective cohort study in U.S used 248 patients as the sample size and followed for two years where out of 248 patients, 47 (19%) were subjected to early unplanned hospital readmission.

There exists a relationship between alcohol abuse and smoking, and increased rate of readmission after hospital admission for patients (Franchi et al., 2013).  The article suggests that patients who excessively use alcohol and smoke stand a chance of being readmitted (Franchi et al., 2013). Sharif et al., (2014) assert that among the key causes of early readmission for the elderly patients are the alcohol abuse and smoking. The two articles by Franchi et al., (2013) and Sharif et al., (2014) evaluate alcohol consumption and smoking claiming that alcohol consumption and smoking increase unplanned hospital readmission. This evaluation is done by assessing the readmission rate of the patients who have never used both alcohol or smoked, those who are currently using, and those who were formers users of the two. Upon the completion of the study, it was found that the rate for unplanned hospital readmission is highest for the current users of alcohol and smokers followed by former users, and lastly those who have never used them. As a result, these articles found other significant factors that are directly or indirectly related to the unplanned hospital readmission.

Three studies examined alcohol consumption and smoking and one of them (Whitlock, 2010) found alcohol consumption and smoking as a significant factor for the unplanned hospital readmission.

2.4.3.5 Surgical Patients

It is important to evaluate whether patients who have been subjected to surgery are prone to readmission because of this factor. As a result, this section reviews studies that have evaluated the relationship between surgery and unplanned hospital readmission.

One study (Zhuang et al., 2015) examined the gastrointestinal surgery in relation to the unplanned hospital readmission of the elderly patients. A prospective study in China by Zhuang et al., (2015) note that it is a common practice of readmitting patients after a gastrointestinal surgery due to the fact that gastrectomy is comprehensive and usually associated with high rates of postoperative mortality and morbidity. Revealing the gastrointestinal surgery as a significant factor, Zhuang et al., (2015) state that one out of seven patients admitted for a surgical procedure is readmitted patients to hospital in a duration of 30 days after discharge.

One study examined gastrointestinal surgery and found gastrointestinal surgery as a significant factor for the unplanned hospital readmission.

2.4.3.6 BMI

BMI (body mass index) refers to the measurement of the body fat grounded on the weight and height of the individual (Clendenne et al., 2015). The results of this measurement is important in revealing whether one is underweight, healthy, or obese. In this section of the study, the effect of BMI on the readmission rate is evaluated.

Three studies (Clendennen, et al. 2015; Van Gestel, et al. 2012; Zhuang, et al. 2015) examined BMI in relation to the unplanned hospital readmission of the elderly patient. One prospective study in China by Zhuang, et al. (2015) found body mass index (BMI) as a significant factor causing unplanned hospital readmission of the elderly patient. The study involved 376 patients who were followed-up for one year and found BMI as a significant factor for patient readmission. The study claimed that increase in age is associated with the increase in BMI. Moreover, the authors claimed that the increase in BMI increases the risk of contracting other nutritional diseases such as bladder problems and mechanical urinary; thus BMI was concluded to be a significant risk factor for unplanned hospital readmission.

Three studies examined BMI as a risk factor and one of them (Zhuang, et al. 2015) found BMI as a significant factor for the unplanned hospital readmission.

2.5 Research Gap

Research gap refers to the problem or a question that has not been answered in a certain field of study (Carey, Yon, Beadles, & Wines, 2012). This void in research is the one that paves a way for a researcher to carry a research. As the reviewed works of literature have shown, readmission is a common occurrence across the globe (Garcia-Perez et al., 2011) severely impacting on health economics. From the articles already reviewed, the risk factors that have been investigated for readmission are age, gender, surgical operation, lack of support, anxiety, and depression, and income, as well as dietary and comorbidities factors. Each study lays emphasis on specific risk factors either psychosocial, demographic, or physical risk factors, and carries a study to assess their effect on readmission rate.

2.6 Conclusion

Unplanned hospital readmission is a prevalent practice, especially for the older adults. As a result, patient readmission has stirred various perceptions from different parties across the world. There exists a variety of risk factors that increase the readmission rates. The predominant factors according to the reviewed studies are age, gender, surgical operation, lack of support, anxiety, depression, and income, as well as dietary among other comorbidities. Assessing the readmitted patients to hospital rate is an effective way of evaluating the effectiveness of treatment.

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